Validation of ACE-FTS satellite data in the upper troposphere/lower stratosphere (UTLS) using non-coincident measurements
Bibliographic record
Abstract
Abstract. CO, O3, and H2O data in the upper troposphere/lower stratosphere (UTLS) measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) on Canada's SCISAT-1 satellite are validated using aircraft measurements. In the UTLS, validation of chemical trace gas measurements is a challenging task due to small-scale variability in the tracer fields, strong gradients of the tracers across the tropopause, and scarcity of measurements suitable for validation purposes. Two alternative methods for the validation of the satellite data are introduced, which avoid the usual need for coincident measurements: tracer-tracer correlations, and vertical profiles relative to the tropopause height. Both largely reduce geophysical variability and thereby provide an "instantaneous climatology", allowing measurement comparison with non-coincident data which yields information about the precision, and a statistically meaningful error-assessment of the ACE-FTS satellite data. We found that the ACE-FTS CO and lower stratospheric O3 agree with the aircraft measurements within ±10% and ±5%, respectively. The ACE-FTS O3 in the UT exhibits a high bias of up to 40%. H2O indicates a low bias with relative differences of around 20% in the LS and 40% in the UT, respectively. When taking into account the smearing effect of the vertically limited spacing between measurements of the ACE-FTS instrument, the errors decrease by 5–15% around the tropopause. The ACE-FTS instrument hence offers unprecedented precision and vertical resolution in the UTLS, that will allow a new global perspective on UTLS tracer distributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".